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“We show that focusing on genes whose expression patterns are evolutionarily conserved across species enhances our ability to learn and predict ‘genes of importance’ to growth performance for staple crops, as well as disease outcomes in animals,” explained Gloria Coruzzi, Carroll & Milton Petrie Professor in NYU’s Department of Biology and Center for Genomics and Systems Biology and the paper’s senior author.

Machine learning can pinpoint “genes of importance” that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture.

Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure—which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge.

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Disaster sciences, digital twins & artificial intelligence — craig fugate, chief emergency management officer, one concern.

Mr. Craig Fugate is the former Director of the Florida Division of Emergency Management, and former administrator of the Federal Emergency Management Agency (FEMA — an agency of the United States Department of Homeland Security, whose primary purpose is to coordinate the response to disasters that have occurred in the United States and that overwhelm the resources of local and state authorities.)

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Bacteria may get a bad reputation in general, yet it’s actually generally healthy and serves an important role in many habitats, including human bodies. From supporting life on Earth to being employed in industrial and medicinal processes, bacteria have their figurative fingers in many pots — some varieties of bacteria can even filter tainted water and make it safe for human consumption.

A team of researchers from the Indian Institute of Technology, Banaras Hindu University (IIT-BHU) has found a bacteria that can do just that — Named “microbacterium paraoxydans strain VSVM IIT (BHU)” by the scientists, it can separate toxic hexavalent chromium from water in an effective and eco-friendly manner, according to a research published in the Journal of Environmental Chemical Engineering.

Hexavalent chromium is a heavy metal ion that is used in electroplating, welding, and chromate painting, among other things. It’s said to be responsible for health problems in humans like cancers, kidney and liver malfunctioning, and infertility. When compared to current approaches, the scientists believe that this bacterial strain, which can tolerate high amounts of hexavalent chromium, is particularly successful at eliminating the harmful substance from wastewater.

A decent chunk of energy usage goes towards lighting, so scientists at MIT are developing a new kind of passive lighting – glow-in-the-dark plants. In the latest experiment, the team has made them glow much brighter than the first generation plants, without harming their health.

The emerging field of “plant nanobionics” involves embedding nanoparticles into plants to give them new abilities. Past work by the MIT team has created plants that can send electrical signals when they need water, spinach that could be used to detect explosives, and watercress that glows in the dark.

As interesting as that last one was, the glow wasn’t particularly bright – about on par with those plastic glowing stars many of us stuck to our ceilings as kids. That’s a cool novelty but not much help for the ultimate use case of passive lighting.

What if you could become invisible to mosquitoes?

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Amit Awasthi, an immunologist with the Translational Health Science and Technology Institute in India and corresponding author of the study, says he and his colleagues pursued this line of inquiry because previous research had linked high salt intake with autoimmune diseases, suggesting that increased salt stimulates immune cells. Meanwhile, tumors are well known to grow in immune-suppressive environments.

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In mice, a diet high in salt suppresses tumor growth—but only when gut microbes are there to stimulate immune cells, a September 10 study in Science Advances reports. The findings raise tantalizing questions about the role of diet and gut microbes in human cancers, and may point to new avenues for therapeutic development.

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This is for all who like coffee:

“To our knowledge, this is the largest study to systematically assess the cardiovascular effects of regular coffee consumption in a population without diagnosed heart disease,” said study author Dr. Judit Simon, of the Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.

Our results suggest that regular coffee consumption is safe, as even high daily intake was not associated with adverse cardiovascular outcomes and all-cause… See More.

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The selected companies will develop lander design concepts, evaluating their performance, design, construction standards, mission assurance requirements, interfaces, safety, crew health accommodations, and medical capabilities. The companies will also mitigate lunar lander risks by conducting critical component tests and advancing the maturity of key technologies.

The work from these companies will ultimately help shape the strategy and requirements for a future NASA’s solicitation to provide regular astronaut transportation from lunar orbit to the surface of the Moon.

The existential threat of COVID-19 has highlighted an acute need to develop working therapeutics against emerging health threats. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds — so long as we can keep up with the viral threat, and access the right data.

As with all new medical maladies, oftentimes the data needs time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) to ask: how can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2?

Typically, data scientists use deep learning to pick out drug combinations with large existing datasets for things like cancer and cardiovascular disease, but, understandably, they can’t be used for new illnesses with limited data.